Nengo: a Python tool for building large-scale functional brain models

Trevor Bekolay, James Bergstra, Eric Hunsberger, Travis DeWolf, Terrence C. Stewart, Daniel Rasmussen, Xuan Choo, Aaron Russell Voelker, Chris Eliasmith
Centre for Theoretical Neuroscience, University of Waterloo, Waterloo, ON, Canada
Frontiers in Neuroinformatics, 2014


   author={Bekolay, Trevor and Bergstra, James and Hunsberger, Eric and DeWolf, Travis and Stewart, Terrence C and Rasmussen, Daniel and Choo, Xuan and Voelker, Aaron and Eliasmith, Chris},

   title={Nengo: A Python tool for building large-scale functional brain models},

   journal={Frontiers in Neuroinformatics},








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Neuroscience currently lacks a comprehensive theory of how cognitive processes can be implemented in a biological substrate. The Neural Engineering Framework (NEF) proposes one such theory, but has not yet gathered significant empirical support, partly due to the technical challenge of building and simulating large-scale models with the NEF. Nengo is a software tool that can be used to build and simulate large-scale models based on the NEF; currently, it is the primary resource for both teaching how the NEF is used, and for doing research that generates specific NEF models to explain experimental data. Nengo 1.4, which was implemented in Java, was used to create Spaun, the world’s largest functional brain model (Eliasmith et al., 2012). Simulating Spaun highlighted limitations in Nengo 1.4’s ability to support model construction with simple syntax, to simulate large models quickly, and to collect large amounts of data for subsequent analysis. This paper describes Nengo 2.0, which is implemented in Python and overcomes these limitations. It uses simple and extendable syntax, simulates a benchmark model on the scale of Spaun 50 times faster than Nengo 1.4, and has a flexible mechanism for collecting simulation results.
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